Affine Arithmetic-based DC Power Flow for Automatic Contingency Selection with Consideration of Load and Generation Uncertainties

Abstract

Power systems operate under uncertainties from variations of loads and generation with time. Power flow in a power system becomes increasingly uncertain, especially when renewable energy, such as wind and photovoltaic generation, is integrated. In this article, a DC power flow approach based on affine arithmetic is proposed to quantify risks due to uncertainties. The proposed method has been applied to the automatic contingency selection under uncertainties. Numerical studies on IEEE test systems (30, 57, and 118 buses) have proved that the DC power flow approach based on affine arithmetic is a fast and reliable method for power system planning and operation when considering uncertainty. Test cases have shown that the proposed method is as fast as the conventional DC power flow while it is much more efficient than the Monte Carlo method. Thus, the DC power flow approach based on affine arithmetic serves as a new and effective decision support tool for planners and operators to cope with high levels of renewable energy penetration, electric vehicle load integration, and other uncertain scenarios.